Статьи

He study aims to revisit the relationship between aviation pollution, tourism, and economic development through the lens of the Environmental Kuznets Curve (EKC), particularly at the regional level, using New Zealand as a case study. We are the first to estimate aviation pollution at regional airports in New Zealand and use them as proxy for the regional pollution in an EKC setting. Our findings provide evidence of an EKC at New Zealand regions, indicating that tourism and economic development contribute to the long-term regional environment improvement. This highlights the necessity for environment policy to be tailored at a regional level, rather than solely at the national scale. Additionally, our research introduces a novel approach to EKC studies by incorporating new pollution estimations, which enhances the understanding of regional environmental dynamics. Among others, we discovered that that the sustainable tourism policy has, and will, work well in New Zealand. This study underscores the importance of considering regional factors in environmental policymaking and offers insights that could inform future strategies for balancing economic growth with environmental sustainability.

The growing economic prominence of BRICS nations (Brazil, Russia, India, China, and South Africa) has attracted considerable attention to the macroeconomic dynamics driving their development. As these economies grow rapidly and become more integrated into global markets, it becomes increasingly difficult to balance economic growth, trade liberalization, and sustainable fiscal policies. Government size, a key factor in fiscal management, tends to increase with national income (as suggested by Wagner’s Law) and in response to trade openness (as outlined by the Compensation Hypothesis). Understanding these dynamics is crucial due to the unique fiscal pressures and global competitiveness faced by BRICS countries. This study investigates the validity of Wagner’s law and the Compensation Hypothesis in the context of BRICS. Using a panel nonlinear autoregressive distributed lag model on annual panel data from 1999 to 2023, our findings confirm Wagner’s law, showing a positive relationship between economic growth and government size. Additionally, the results support the Compensation Hypothesis, indicating that trade openness enhances government size. This study underscores the potential trade-offs between promoting economic growth and trade liberalization, as these strategies may inadvertently expand the government sector and affect fiscal stability. As BRICS economies continue to integrate into global markets, this research contributes to the discussion on Wagner’s law and trade openness, offering new insights into sustainable fiscal policies, government expenditure optimization, and the pursuit of global competitiveness and economic growth within the BRICS framework.

Megumi Saigo derived generalized fractional operators, involving Gauss hypergeometric function, having four special cases: Riemann-Liouville, Weyl, Erdely-Kober left and right sided fractional operators. Mridula Garg and Lata Chanchalani established q-analogues of Saigo fractional integral operators. Building upon this base, the current article aims to generalize Saigo integral operators as well their q-analogues. In addition, we obtain some new results involving extended Saigo integral operators and their q-extensions.

Innovation processes are strongly in uenced by changes in economic, political, technological and other external factors. For instance, economic instability and political uncertainty can both stimulate and limit innovative activity in organisations. Transmodern innovation is a concept that involves scienti c and technological advancements that may remain unutilised until favourable changes occur in technological or economic conditions. The purpose of this study is to develop a conceptual model for transmodern innovation that takes into account the dynamics of innovation, including the intensity, economic prerequisites, external changes and degree of innovation adaptation. This model will help organisations to better understand and respond to the complexities of the innovation process. The resulting model is a comprehensive tool for analysing changes in innovation activity and the external environment over di erent time phases, including the initial state (t0), the transition to new conditions (t1) and the nal state (tx). In this model, the ‘Final stage of tx’ block represents the nal stage, which allows us to draw conclusions about the success of adaptation and innovation development. This is the basis for formulating strategic conclusions and recommendations for future development.

This article analyses the sustainability of the agro-industrial complex (AIC) in the Eurasian Economic Union (EAEU) countries with an emphasis on food security. The study covers challenges and threats to food security in Russia, Belarus, Armenia, Kazakhstan, and Kyrgyzstan, given the difficult geopolitical situation. The article examines data from the national statistical services of the EAEU countries, as well as international sources such as the FAO and the World Bank. Correlation and cluster analysis approaches are applied to assess the impact of socioeconomic indicators on the sustainability of the AIC. Significant correlations between indicators of food security and such factors as the volume of agricultural production, investments in the agricultural sector, the level of technological development, and government support are revealed. On average, for the period from 2015 to 2022, the added value of agriculture amounted to 8.2% of GDP, and the food production index was 104.1. The results of the cluster analysis showed that the EAEU countries can be grouped by levels of agricultural development and food security. Thus, K-means and GMM identified three clusters in which Russia found itself both in a separate cluster and in combination with other countries. Agglomerative and spectral clustering also showed similar results, distinguishing three main groups of countries. The average silhouette coefficient for agglomerative and spectral clustering was 0.41, which indicates a better clustering quality compared to K-means and GMM (0.38). It is confirmed that integration and coordination of efforts within the EAEU, as well as diversification of agricultural production and increased investment in innovation, determine the state of sustainability of the agro-industrial complex.

The contemporary information landscape is characterised by a huge amount of data available for analysis using a variety of research tools and methods. Considering the limitations of using individual models and methods, it is worth employing an approach that combines functional and logical autoregression methods to conduct a more accurate analysis of trends and topics in the information space. Considering this context, this work aims to develop an algorithm to identify and analyse topics that would be relevant in the future using autoregression methods. The process begins with the quantification and normalisation of data, which significantly affect the quality of analysis. The main focus of this study is to implement the autoregression method to analyse long-term trends and predict future developments in the selected data. The proposed algorithm evaluates the forecast of these future developments and analyses graphical trends, thus conducting a more detailed study and modelling of future data dynamics. The regression coefficient is used as a quality criterion. The algorithm concludes with a polynomial function to help identify topics that will be relevant in the future. Overall, the proposed algorithm can be considered an effective tool for analysing and predicting future trends based on the analysis of historical data, thus contributing to the identification of prospects for technological development.

This article discusses the relevance of mapping as a lean technology employed in the higher education institution in the conditions of digital transformation. The authors emphasize that modern challenges require optimization of business processes, which can be achieved by using lean production methods. In the course of the research a mapping tool was used to analyze and optimize the tracking of student attendance in the structural divisions of the university. This work aims to improve control over student attendance, including several major tasks: assessment of existing lean production tools, application of mapping in attendance tracking, optimization of the current control measures, and development of recommendations for further improvement based on the PDCA cycle. According to the results, mapping and the PDCA cycle proved their efficiency in terms of improving the quality of education in the digital environment.

In recent years artificial intelligence (AI) has become an indispensable tool in digital marketing that is able to simplify human performance and expand business opportunities. This research considers the current AI (artificial intelligence) architectures in digital marketing, reflects on their impact on the activities of companies, and develops a range of optimization recommendations. The authors identify the most important tasks in evaluating existing solutions and their efficiency, as well as assess the possibilities of switching to AI technologies in business. Specific attention is also devoted to the examples of the neural networks implementation in marketing. As a result, the main components of the AI support architecture are identified, together with the further development prospects, with due consideration of current trends and ethical aspects. This research employs the practical achievements of marketing specialists and suggests a range of step-by-step strategies to optimize the business processes.

The healthcare industry makes one of the main components of the productive forces of the state. Therefore, the well-being and welfare of the entire society in the future depend on its thriving development. Despite significant accumulated knowledge in medicine, there are still some white spots that are difficult to analyze and predict. The use of artificial intelligence and neural networks in healthcare can significantly expand the analytical apparatus and radically change the existing approaches to scientific research. This article discusses the results of the practical application of artificial intelligence and artificial neural networks in healthcare. The research aims to demonstrate the prospects and advantages of using these information technologies in medicine; identify problems in the implementation of AI technologies in medical practice and offer possible solutions to some of them. The authors provide a comprehensive literature review on the issues of artificial intelligence and neural networks, consider successful examples of the AI use in pharmacology, forecasting, and research of various types of diseases, including cardiovascular system, dermatology, and oncology. A significant part of the research is devoted to ethical and legal concerns, as well as problems associated with the practical use of artificial intelligence. As a result of the research, the authors suggest the models of the IT architecture of a medical information system and data flows, based on the TOGAF standard.

Machine learning (ML) environments offer a variety of methods and tools that help to solve problems in different areas, including software engineering (SE). Currently, a large number of researchers are interested in the possibilities of using various machine learning techniques in software engineering. This paper provides an overview of machine learning techniques used in each stage of the software development life cycle (SDLC). The contribution of this review is significant. Firstly, by analyzing sources from bibliographic and abstract databases, it was found that the topic of integrating machine learning techniques into software engineering is relevant. Secondly, the article poses questions and reviews the methodology of this research. In addition, machine learning methods are systematized according to their application at each stage of software development. Despite the vast amount of research work on the use of machine learning techniques in software engineering, further research is required to achieve comprehensive comparisons and synergies of the approaches used, meaningful evaluations based on detailed practical implementations that could be adopted by the industry. Thus, future efforts should be directed towards reproducible research rather than isolated new ideas. Otherwise, most of these applications will remain poorly realized in practice.

Первая и вторая статьи посвящены анализу роли творчества А. Стриндберга в дискуссии о «новой женщине» в России рубежа XIX-XX вв. В научной литературе был неоднократно отмечен интерес русских авторов этого периода к творчеству шведского писателя, однако в вопросе рецепции взглядов Стриндберга на женщину остаются неисследованные области. Обсуждение новой роли женщины в эпоху активных общественных изменений в России оказалось встречным течением, определившим особенное внимание к некоторым произведениям Стриндберга, в частности пьесе «Фрекен Жюли». В первой статье рассматриваются взгляды российских современников Стриндберга на изображение женщины в его творчестве и затрагивается история переводов и постановок «Фрекен Жюли» в России. Во второй статье мы обращаем внимание на влияние этой драмы на образы пьес «Вишневый сад» А. П. Чехова и «Жан Ермолаев» Г. Ге и концентрируемся на рассказе А. В. Амфитеатрова «Нелли Раинцева» (позднее превращенном автором в сценарий и Е. Бауэром - в фильм), сюжет которого, как впервые показано в статье, является вариацией сюжета стриндберговской «Фрекен Жюли». В качестве доказательств приводятся фрагменты дискуссии современников Амфитеатрова о женоненавистничестве Стриндберга, в частности письмо Горького к Амфитеатрову, и публицистические размышления самого Амфитеатрова о женских правах. Подвергаются анализу изменения, привнесенные Амфитеатровом в стриндберговский сюжет и характеры, и делается вывод о том, что Амфитеатров оправдывает «падшую» аристократку, в т. ч. путем предоставления слова ей самой с помощью дневниковой формы, нередко использовавшейся в то время в литературе и кино о новых женщинах. Делается вывод о том, как сюжет и характеры пьесы «Фрекен Жюли» были преобразованы в русской культуре.

The analysis of land use and land cover (LULC) based on remote sensing and geographic information systems in Ben En National Park, Vietnam, from 2003 to 2023 has revealed significant landscape changes. Assessing the accuracy of the classification results on our Landsat satellite images has shown high reliability, with kappa coefficients above 0.9 for both 2003 and 2023, indicating strong agreement between the classified images and actual reference data. Over the two-decade period, the dominant LULC class remained natural forest, albeit experiencing a substantial reduction in coverage. In contrast, waterbodies and agricultural land expanded significantly. These LULC changes can be attributed to both natural processes and human activities, such as dam construction and water management projects. The most concerning trend is the significant decline in natural forest coverage, primarily driven by deforestation, logging, and land conversion. These activities pose a severe threat to plant biodiversity and the habitats of wildlife within Ben En National Park. Climate change, characterized by erratic weather patterns, exacerbates these challenges, disrupting forest development. Prolonged droughts and heavy rainfall disrupt the growth of planted species, aggravating the situation. Urgent measures are required to address illegal logging and deforestation, coupled with sustainable land management practices to safeguard the park’s unique biodiversity. This study underscores the importance of remote sensing and geographic information systems in monitoring and addressing environmental changes, providing essential data for informed decision-making in land use planning and conservation efforts within the national park.